{
 "metadata": {
  "name": "",
  "signature": "sha256:2d740a5aef0d46968d828c0b505f14d0d1589e346da0cb6ac1058fa6adb75030"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mkdir -p _temp\n",
      "!echo filename,xmin,ymin,xmax,ymax > _temp/det_input.csv\n",
      "!echo `pwd`/images/fish-bike.jpg, 230, 9, 330, 150 >> _temp/det_input.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!../python/detect.py --crop_mode=list --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.csv _temp/det_output.h5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "WARNING: Logging before InitGoogleLogging() is written to STDERR\r\n",
        "I1218 15:25:50.179925  7760 net.cpp:39] Initializing net from parameters: \r\n",
        "name: \"R-CNN-ilsvrc13\"\r\n",
        "layers {\r\n",
        "  bottom: \"data\"\r\n",
        "  top: \"conv1\"\r\n",
        "  name: \"conv1\"\r\n",
        "  type: CONVOLUTION\r\n",
        "  convolution_param {\r\n",
        "    num_output: 96\r\n",
        "    kernel_size: 11\r\n",
        "    stride: 4\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"conv1\"\r\n",
        "  top: \"conv1\"\r\n",
        "  name: \"relu1\"\r\n",
        "  type: RELU\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"conv1\"\r\n",
        "  top: \"pool1\"\r\n",
        "  name: \"pool1\"\r\n",
        "  type: POOLING\r\n",
        "  pooling_param {\r\n",
        "    pool: MAX\r\n",
        "    kernel_size: 3\r\n",
        "    stride: 2\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"pool1\"\r\n",
        "  top: \"norm1\"\r\n",
        "  name: \"norm1\"\r\n",
        "  type: LRN\r\n",
        "  lrn_param {\r\n",
        "    local_size: 5\r\n",
        "    alpha: 0.0001\r\n",
        "    beta: 0.75\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"norm1\"\r\n",
        "  top: \"conv2\"\r\n",
        "  name: \"conv2\"\r\n",
        "  type: CONVOLUTION\r\n",
        "  convolution_param {\r\n",
        "    num_output: 256\r\n",
        "    pad: 2\r\n",
        "    kernel_size: 5\r\n",
        "    group: 2\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"conv2\"\r\n",
        "  top: \"conv2\"\r\n",
        "  name: \"relu2\"\r\n",
        "  type: RELU\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"conv2\"\r\n",
        "  top: \"pool2\"\r\n",
        "  name: \"pool2\"\r\n",
        "  type: POOLING\r\n",
        "  pooling_param {\r\n",
        "    pool: MAX\r\n",
        "    kernel_size: 3\r\n",
        "    stride: 2\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"pool2\"\r\n",
        "  top: \"norm2\"\r\n",
        "  name: \"norm2\"\r\n",
        "  type: LRN\r\n",
        "  lrn_param {\r\n",
        "    local_size: 5\r\n",
        "    alpha: 0.0001\r\n",
        "    beta: 0.75\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"norm2\"\r\n",
        "  top: \"conv3\"\r\n",
        "  name: \"conv3\"\r\n",
        "  type: CONVOLUTION\r\n",
        "  convolution_param {\r\n",
        "    num_output: 384\r\n",
        "    pad: 1\r\n",
        "    kernel_size: 3\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"conv3\"\r\n",
        "  top: \"conv3\"\r\n",
        "  name: \"relu3\"\r\n",
        "  type: RELU\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"conv3\"\r\n",
        "  top: \"conv4\"\r\n",
        "  name: \"conv4\"\r\n",
        "  type: CONVOLUTION\r\n",
        "  convolution_param {\r\n",
        "    num_output: 384\r\n",
        "    pad: 1\r\n",
        "    kernel_size: 3\r\n",
        "    group: 2\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"conv4\"\r\n",
        "  top: \"conv4\"\r\n",
        "  name: \"relu4\"\r\n",
        "  type: RELU\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"conv4\"\r\n",
        "  top: \"conv5\"\r\n",
        "  name: \"conv5\"\r\n",
        "  type: CONVOLUTION\r\n",
        "  convolution_param {\r\n",
        "    num_output: 256\r\n",
        "    pad: 1\r\n",
        "    kernel_size: 3\r\n",
        "    group: 2\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"conv5\"\r\n",
        "  top: \"conv5\"\r\n",
        "  name: \"relu5\"\r\n",
        "  type: RELU\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"conv5\"\r\n",
        "  top: \"pool5\"\r\n",
        "  name: \"pool5\"\r\n",
        "  type: POOLING\r\n",
        "  pooling_param {\r\n",
        "    pool: MAX\r\n",
        "    kernel_size: 3\r\n",
        "    stride: 2\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"pool5\"\r\n",
        "  top: \"fc6\"\r\n",
        "  name: \"fc6\"\r\n",
        "  type: INNER_PRODUCT\r\n",
        "  inner_product_param {\r\n",
        "    num_output: 4096\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"fc6\"\r\n",
        "  top: \"fc6\"\r\n",
        "  name: \"relu6\"\r\n",
        "  type: RELU\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"fc6\"\r\n",
        "  top: \"fc6\"\r\n",
        "  name: \"drop6\"\r\n",
        "  type: DROPOUT\r\n",
        "  dropout_param {\r\n",
        "    dropout_ratio: 0.5\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"fc6\"\r\n",
        "  top: \"fc7\"\r\n",
        "  name: \"fc7\"\r\n",
        "  type: INNER_PRODUCT\r\n",
        "  inner_product_param {\r\n",
        "    num_output: 4096\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"fc7\"\r\n",
        "  top: \"fc7\"\r\n",
        "  name: \"relu7\"\r\n",
        "  type: RELU\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"fc7\"\r\n",
        "  top: \"fc7\"\r\n",
        "  name: \"drop7\"\r\n",
        "  type: DROPOUT\r\n",
        "  dropout_param {\r\n",
        "    dropout_ratio: 0.5\r\n",
        "  }\r\n",
        "}\r\n",
        "layers {\r\n",
        "  bottom: \"fc7\"\r\n",
        "  top: \"fc-rcnn\"\r\n",
        "  name: \"fc-rcnn\"\r\n",
        "  type: INNER_PRODUCT\r\n",
        "  inner_product_param {\r\n",
        "    num_output: 200\r\n",
        "  }\r\n",
        "}\r\n",
        "input: \"data\"\r\n",
        "input_dim: 10\r\n",
        "input_dim: 3\r\n",
        "input_dim: 227\r\n",
        "input_dim: 227\r\n",
        "I1218 15:25:50.180410  7760 net.cpp:358] Input 0 -> data\r\n",
        "I1218 15:25:50.180443  7760 net.cpp:67] Creating Layer conv1\r\n",
        "I1218 15:25:50.180451  7760 net.cpp:394] conv1 <- data\r\n",
        "I1218 15:25:50.180461  7760 net.cpp:356] conv1 -> conv1\r\n",
        "I1218 15:25:50.180471  7760 net.cpp:96] Setting up conv1\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I1218 15:25:50.200556  7760 net.cpp:103] Top shape: 10 96 55 55 (2904000)\r\n",
        "I1218 15:25:50.200626  7760 net.cpp:67] Creating Layer relu1\r\n",
        "I1218 15:25:50.200636  7760 net.cpp:394] relu1 <- conv1\r\n",
        "I1218 15:25:50.200647  7760 net.cpp:345] relu1 -> conv1 (in-place)\r\n",
        "I1218 15:25:50.200660  7760 net.cpp:96] Setting up relu1\r\n",
        "I1218 15:25:50.200673  7760 net.cpp:103] Top shape: 10 96 55 55 (2904000)\r\n",
        "I1218 15:25:50.200685  7760 net.cpp:67] Creating Layer pool1\r\n",
        "I1218 15:25:50.200692  7760 net.cpp:394] pool1 <- conv1\r\n",
        "I1218 15:25:50.200700  7760 net.cpp:356] pool1 -> pool1\r\n",
        "I1218 15:25:50.200711  7760 net.cpp:96] Setting up pool1\r\n",
        "I1218 15:25:50.200726  7760 net.cpp:103] Top shape: 10 96 27 27 (699840)\r\n",
        "I1218 15:25:50.200736  7760 net.cpp:67] Creating Layer norm1\r\n",
        "I1218 15:25:50.200743  7760 net.cpp:394] norm1 <- pool1\r\n",
        "I1218 15:25:50.200752  7760 net.cpp:356] norm1 -> norm1\r\n",
        "I1218 15:25:50.200760  7760 net.cpp:96] Setting up norm1\r\n",
        "I1218 15:25:50.200770  7760 net.cpp:103] Top shape: 10 96 27 27 (699840)\r\n",
        "I1218 15:25:50.200780  7760 net.cpp:67] Creating Layer conv2\r\n",
        "I1218 15:25:50.200788  7760 net.cpp:394] conv2 <- norm1\r\n",
        "I1218 15:25:50.200798  7760 net.cpp:356] conv2 -> conv2\r\n",
        "I1218 15:25:50.200808  7760 net.cpp:96] Setting up conv2\r\n",
        "I1218 15:25:50.201256  7760 net.cpp:103] Top shape: 10 256 27 27 (1866240)\r\n",
        "I1218 15:25:50.201283  7760 net.cpp:67] Creating Layer relu2\r\n",
        "I1218 15:25:50.201292  7760 net.cpp:394] relu2 <- conv2\r\n",
        "I1218 15:25:50.201300  7760 net.cpp:345] relu2 -> conv2 (in-place)\r\n",
        "I1218 15:25:50.201308  7760 net.cpp:96] Setting up relu2\r\n",
        "I1218 15:25:50.201318  7760 net.cpp:103] Top shape: 10 256 27 27 (1866240)\r\n",
        "I1218 15:25:50.201326  7760 net.cpp:67] Creating Layer pool2\r\n",
        "I1218 15:25:50.201333  7760 net.cpp:394] pool2 <- conv2\r\n",
        "I1218 15:25:50.201342  7760 net.cpp:356] pool2 -> pool2\r\n",
        "I1218 15:25:50.201350  7760 net.cpp:96] Setting up pool2\r\n",
        "I1218 15:25:50.201360  7760 net.cpp:103] Top shape: 10 256 13 13 (432640)\r\n",
        "I1218 15:25:50.201370  7760 net.cpp:67] Creating Layer norm2\r\n",
        "I1218 15:25:50.201376  7760 net.cpp:394] norm2 <- pool2\r\n",
        "I1218 15:25:50.201385  7760 net.cpp:356] norm2 -> norm2\r\n",
        "I1218 15:25:50.201395  7760 net.cpp:96] Setting up norm2\r\n",
        "I1218 15:25:50.201401  7760 net.cpp:103] Top shape: 10 256 13 13 (432640)\r\n",
        "I1218 15:25:50.201412  7760 net.cpp:67] Creating Layer conv3\r\n",
        "I1218 15:25:50.201419  7760 net.cpp:394] conv3 <- norm2\r\n",
        "I1218 15:25:50.201427  7760 net.cpp:356] conv3 -> conv3\r\n",
        "I1218 15:25:50.201436  7760 net.cpp:96] Setting up conv3\r\n",
        "I1218 15:25:50.202339  7760 net.cpp:103] Top shape: 10 384 13 13 (648960)\r\n",
        "I1218 15:25:50.202379  7760 net.cpp:67] Creating Layer relu3\r\n",
        "I1218 15:25:50.202389  7760 net.cpp:394] relu3 <- conv3\r\n",
        "I1218 15:25:50.202399  7760 net.cpp:345] relu3 -> conv3 (in-place)\r\n",
        "I1218 15:25:50.202407  7760 net.cpp:96] Setting up relu3\r\n",
        "I1218 15:25:50.202416  7760 net.cpp:103] Top shape: 10 384 13 13 (648960)\r\n",
        "I1218 15:25:50.202426  7760 net.cpp:67] Creating Layer conv4\r\n",
        "I1218 15:25:50.202433  7760 net.cpp:394] conv4 <- conv3\r\n",
        "I1218 15:25:50.202445  7760 net.cpp:356] conv4 -> conv4\r\n",
        "I1218 15:25:50.202455  7760 net.cpp:96] Setting up conv4\r\n",
        "I1218 15:25:50.203539  7760 net.cpp:103] Top shape: 10 384 13 13 (648960)\r\n",
        "I1218 15:25:50.203577  7760 net.cpp:67] Creating Layer relu4\r\n",
        "I1218 15:25:50.203585  7760 net.cpp:394] relu4 <- conv4\r\n",
        "I1218 15:25:50.203603  7760 net.cpp:345] relu4 -> conv4 (in-place)\r\n",
        "I1218 15:25:50.203613  7760 net.cpp:96] Setting up relu4\r\n",
        "I1218 15:25:50.203622  7760 net.cpp:103] Top shape: 10 384 13 13 (648960)\r\n",
        "I1218 15:25:50.203635  7760 net.cpp:67] Creating Layer conv5\r\n",
        "I1218 15:25:50.203644  7760 net.cpp:394] conv5 <- conv4\r\n",
        "I1218 15:25:50.203652  7760 net.cpp:356] conv5 -> conv5\r\n",
        "I1218 15:25:50.203661  7760 net.cpp:96] Setting up conv5\r\n",
        "I1218 15:25:50.204293  7760 net.cpp:103] Top shape: 10 256 13 13 (432640)\r\n",
        "I1218 15:25:50.204325  7760 net.cpp:67] Creating Layer relu5\r\n",
        "I1218 15:25:50.204334  7760 net.cpp:394] relu5 <- conv5\r\n",
        "I1218 15:25:50.204344  7760 net.cpp:345] relu5 -> conv5 (in-place)\r\n",
        "I1218 15:25:50.204352  7760 net.cpp:96] Setting up relu5\r\n",
        "I1218 15:25:50.204361  7760 net.cpp:103] Top shape: 10 256 13 13 (432640)\r\n",
        "I1218 15:25:50.204371  7760 net.cpp:67] Creating Layer pool5\r\n",
        "I1218 15:25:50.204378  7760 net.cpp:394] pool5 <- conv5\r\n",
        "I1218 15:25:50.204386  7760 net.cpp:356] pool5 -> pool5\r\n",
        "I1218 15:25:50.204396  7760 net.cpp:96] Setting up pool5\r\n",
        "I1218 15:25:50.204406  7760 net.cpp:103] Top shape: 10 256 6 6 (92160)\r\n",
        "I1218 15:25:50.204417  7760 net.cpp:67] Creating Layer fc6\r\n",
        "I1218 15:25:50.204424  7760 net.cpp:394] fc6 <- pool5\r\n",
        "I1218 15:25:50.204432  7760 net.cpp:356] fc6 -> fc6\r\n",
        "I1218 15:25:50.204442  7760 net.cpp:96] Setting up fc6\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I1218 15:25:50.277786  7760 net.cpp:103] Top shape: 10 4096 1 1 (40960)\r\n",
        "I1218 15:25:50.277849  7760 net.cpp:67] Creating Layer relu6\r\n",
        "I1218 15:25:50.277859  7760 net.cpp:394] relu6 <- fc6\r\n",
        "I1218 15:25:50.277869  7760 net.cpp:345] relu6 -> fc6 (in-place)\r\n",
        "I1218 15:25:50.277881  7760 net.cpp:96] Setting up relu6\r\n",
        "I1218 15:25:50.277899  7760 net.cpp:103] Top shape: 10 4096 1 1 (40960)\r\n",
        "I1218 15:25:50.277911  7760 net.cpp:67] Creating Layer drop6\r\n",
        "I1218 15:25:50.277919  7760 net.cpp:394] drop6 <- fc6\r\n",
        "I1218 15:25:50.277926  7760 net.cpp:345] drop6 -> fc6 (in-place)\r\n",
        "I1218 15:25:50.277935  7760 net.cpp:96] Setting up drop6\r\n",
        "I1218 15:25:50.277943  7760 net.cpp:103] Top shape: 10 4096 1 1 (40960)\r\n",
        "I1218 15:25:50.277952  7760 net.cpp:67] Creating Layer fc7\r\n",
        "I1218 15:25:50.277959  7760 net.cpp:394] fc7 <- fc6\r\n",
        "I1218 15:25:50.277967  7760 net.cpp:356] fc7 -> fc7\r\n",
        "I1218 15:25:50.277976  7760 net.cpp:96] Setting up fc7\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I1218 15:25:50.300503  7760 net.cpp:103] Top shape: 10 4096 1 1 (40960)\r\n",
        "I1218 15:25:50.300575  7760 net.cpp:67] Creating Layer relu7\r\n",
        "I1218 15:25:50.300586  7760 net.cpp:394] relu7 <- fc7\r\n",
        "I1218 15:25:50.300598  7760 net.cpp:345] relu7 -> fc7 (in-place)\r\n",
        "I1218 15:25:50.300611  7760 net.cpp:96] Setting up relu7\r\n",
        "I1218 15:25:50.300629  7760 net.cpp:103] Top shape: 10 4096 1 1 (40960)\r\n",
        "I1218 15:25:50.300639  7760 net.cpp:67] Creating Layer drop7\r\n",
        "I1218 15:25:50.300647  7760 net.cpp:394] drop7 <- fc7\r\n",
        "I1218 15:25:50.300654  7760 net.cpp:345] drop7 -> fc7 (in-place)\r\n",
        "I1218 15:25:50.300662  7760 net.cpp:96] Setting up drop7\r\n",
        "I1218 15:25:50.300670  7760 net.cpp:103] Top shape: 10 4096 1 1 (40960)\r\n",
        "I1218 15:25:50.300680  7760 net.cpp:67] Creating Layer fc-rcnn\r\n",
        "I1218 15:25:50.300688  7760 net.cpp:394] fc-rcnn <- fc7\r\n",
        "I1218 15:25:50.300696  7760 net.cpp:356] fc-rcnn -> fc-rcnn\r\n",
        "I1218 15:25:50.300706  7760 net.cpp:96] Setting up fc-rcnn\r\n",
        "I1218 15:25:50.301726  7760 net.cpp:103] Top shape: 10 200 1 1 (2000)\r\n",
        "I1218 15:25:50.301749  7760 net.cpp:172] fc-rcnn does not need backward computation.\r\n",
        "I1218 15:25:50.301758  7760 net.cpp:172] drop7 does not need backward computation.\r\n",
        "I1218 15:25:50.301764  7760 net.cpp:172] relu7 does not need backward computation.\r\n",
        "I1218 15:25:50.301771  7760 net.cpp:172] fc7 does not need backward computation.\r\n",
        "I1218 15:25:50.301777  7760 net.cpp:172] drop6 does not need backward computation.\r\n",
        "I1218 15:25:50.301784  7760 net.cpp:172] relu6 does not need backward computation.\r\n",
        "I1218 15:25:50.301791  7760 net.cpp:172] fc6 does not need backward computation.\r\n",
        "I1218 15:25:50.301797  7760 net.cpp:172] pool5 does not need backward computation.\r\n",
        "I1218 15:25:50.301805  7760 net.cpp:172] relu5 does not need backward computation.\r\n",
        "I1218 15:25:50.301811  7760 net.cpp:172] conv5 does not need backward computation.\r\n",
        "I1218 15:25:50.301818  7760 net.cpp:172] relu4 does not need backward computation.\r\n",
        "I1218 15:25:50.301826  7760 net.cpp:172] conv4 does not need backward computation.\r\n",
        "I1218 15:25:50.301831  7760 net.cpp:172] relu3 does not need backward computation.\r\n",
        "I1218 15:25:50.301838  7760 net.cpp:172] conv3 does not need backward computation.\r\n",
        "I1218 15:25:50.301846  7760 net.cpp:172] norm2 does not need backward computation.\r\n",
        "I1218 15:25:50.301852  7760 net.cpp:172] pool2 does not need backward computation.\r\n",
        "I1218 15:25:50.301858  7760 net.cpp:172] relu2 does not need backward computation.\r\n",
        "I1218 15:25:50.301866  7760 net.cpp:172] conv2 does not need backward computation.\r\n",
        "I1218 15:25:50.301872  7760 net.cpp:172] norm1 does not need backward computation.\r\n",
        "I1218 15:25:50.301878  7760 net.cpp:172] pool1 does not need backward computation.\r\n",
        "I1218 15:25:50.301884  7760 net.cpp:172] relu1 does not need backward computation.\r\n",
        "I1218 15:25:50.301892  7760 net.cpp:172] conv1 does not need backward computation.\r\n",
        "I1218 15:25:50.301898  7760 net.cpp:208] This network produces output fc-rcnn\r\n",
        "I1218 15:25:50.301913  7760 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
        "I1218 15:25:50.301921  7760 net.cpp:219] Network initialization done.\r\n",
        "I1218 15:25:50.301928  7760 net.cpp:220] Memory required for data: 62425920\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "GPU mode\r\n",
        "Loading input...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[('/media/neil/Working Space/Playground/Codes/Caffe/examples/images/fish-bike.jpg', array([[  9, 230, 150, 330]]))]\r\n",
        "Processed 1 windows in 0.116 s.\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/usr/local/lib/python2.7/dist-packages/pandas/io/pytables.py:2486: PerformanceWarning: \r\n",
        "your performance may suffer as PyTables will pickle object types that it cannot\r\n",
        "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\r\n",
        "\r\n",
        "  warnings.warn(ws, PerformanceWarning)\r\n",
        "Saved to _temp/det_output.h5 in 0.045 s.\r\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
      "print(df.shape)\n",
      "print(df.iloc[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1, 5)\n",
        "prediction    [-1.95746, -2.50224, -2.19327, -1.95178, -1.67...\n",
        "ymin                                                          9\n",
        "xmin                                                        230\n",
        "ymax                                                        150\n",
        "xmax                                                        330\n",
        "Name: /media/neil/Working Space/Playground/Codes/Caffe/examples/images/fish-bike.jpg, dtype: object\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n",
      "    labels_df = pd.DataFrame([\n",
      "        {\n",
      "            'synset_id': l.strip().split(' ')[0],\n",
      "            'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n",
      "        }\n",
      "        for l in f.readlines()\n",
      "    ])\n",
      "labels_df.sort('synset_id')\n",
      "predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\n",
      "print(predictions_df.iloc[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "array([[-1.95745873, -2.50224352, -2.19327164, -1.9517839 , -1.6729126 ,\n",
        "        -1.81325698, -1.83288765, -2.00196433, -2.56944418, -1.75048864,\n",
        "        -1.82330239, -1.77087784, -1.98228717, -1.60661113, -1.65453529,\n",
        "        -1.54234505, -1.77228594, -2.24971342, -1.76821208, -2.15888453,\n",
        "        -2.38012028, -1.5427649 , -1.82162285, -1.87951219, -1.56205773,\n",
        "        -2.72787666, -1.39359999, -1.62907231, -2.15417719, -1.78918827,\n",
        "        -1.60221899, -2.18512273, -1.69293022, -1.97533941, -2.07003546,\n",
        "        -2.06015348, -2.18613625, -2.3134973 , -2.4461329 , -1.89933753,\n",
        "        -2.13332176, -2.05881   , -1.98704767, -2.29805303, -1.64619052,\n",
        "        -2.32312274, -1.97030258, -2.1246655 , -2.06664491, -2.10120392,\n",
        "        -1.76447296, -1.92135739, -1.72563446, -1.90520096, -2.0754652 ,\n",
        "        -1.95325959, -2.21113348, -2.72237158, -1.55881596, -2.00045991,\n",
        "        -2.12474895, -1.9929384 , -1.96791494, -2.13965273, -1.35890019,\n",
        "        -1.79466891, -1.65795672, -1.67063713, -1.55574059, -1.79353333,\n",
        "        -1.69570529, -2.11883092, -1.65271819, -2.24223828, -1.87331176,\n",
        "        -1.58437777, -2.29902411, -2.06372881, -2.02690601, -1.43223643,\n",
        "        -1.64574528, -1.85451388, -1.61519814, -1.89117277, -1.53103316,\n",
        "        -2.11973047, -2.27997756, -1.65987635, -2.3655448 , -2.06371403,\n",
        "        -1.37248731, -2.08089542, -1.81507349, -1.65879107, -2.0786922 ,\n",
        "        -2.28271914, -2.08089781, -1.72873497, -2.12617898, -2.09848881,\n",
        "        -2.17397594, -1.85255587, -1.93553567, -1.629017  , -1.79608905,\n",
        "        -2.07499599, -2.03953671, -1.76923048, -1.65250278, -2.09576297,\n",
        "        -1.98192942, -1.6711477 , -1.4759829 , -2.33585262, -1.90136373,\n",
        "        -1.71581101, -1.68082619, -2.07732677, -1.68049562, -1.73632038,\n",
        "        -1.67344141, -2.03164911, -1.9092741 ,  0.42654228, -2.66412115,\n",
        "        -1.77708054, -1.67212558, -1.92680073, -1.6382463 , -1.48829937,\n",
        "        -1.99544728, -1.62861633, -1.67376196, -1.9241147 , -1.61942923,\n",
        "        -1.77051401, -1.83308268, -1.38650894, -2.16704011, -1.38600934,\n",
        "        -1.91936231, -1.6992588 , -2.23598242, -2.25815821, -2.07228947,\n",
        "        -1.83936977, -1.27036679, -2.24582767, -1.5833441 , -2.24116182,\n",
        "        -2.23493147, -2.13052201, -1.75795388, -1.98138428, -2.1553452 ,\n",
        "        -1.52460766, -1.97123671, -2.12915683, -2.27556801, -1.91776156,\n",
        "        -2.00239325, -1.69318402, -1.80556941, -2.25343871, -1.38369536,\n",
        "        -2.07829046, -1.89499259, -2.05027056, -2.03059554, -1.96320629,\n",
        "        -1.6162169 , -1.75643289, -1.73409319, -1.26926875, -1.95805526,\n",
        "        -1.58217478, -1.65501928, -2.16072869, -1.63144469, -1.8738327 ,\n",
        "        -1.59910727, -1.74189591, -1.94644153, -2.01262164, -1.71318269,\n",
        "        -1.87062097, -1.67657042, -1.59016109, -1.86647105, -1.87806487,\n",
        "        -1.70441771, -1.87123847, -2.30646634, -1.94736505, -1.6825099 ,\n",
        "        -1.78767276, -2.44528461, -2.47638273, -1.80605555, -1.78828573]], dtype=float32)"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "max_s = predictions_df.max(0)\n",
      "max_s.sort(ascending=False)\n",
      "print(max_s[:10])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "name\n",
        "person             0.426542\n",
        "swimming trunks   -1.269269\n",
        "rubber eraser     -1.270367\n",
        "face powder       -1.358900\n",
        "horizontal bar    -1.372487\n",
        "spatula           -1.383695\n",
        "purse             -1.386009\n",
        "puck              -1.386509\n",
        "bookshelf         -1.393600\n",
        "hair dryer        -1.432236\n",
        "dtype: float32\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Find, print, and display the top detections: person and bicycle.\n",
      "i = predictions_df['person'].argmax()\n",
      "j = predictions_df['bicycle'].argmax()\n",
      "\n",
      "# Show top predictions for top detection.\n",
      "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
      "print('Top detection:')\n",
      "print(f.order(ascending=False)[:5])\n",
      "print('')\n",
      "\n",
      "# Show top predictions for second-best detection.\n",
      "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n",
      "print('Second-best detection:')\n",
      "print(f.order(ascending=False)[:5])\n",
      "\n",
      "# Show top detection in red, second-best top detection in blue.\n",
      "im = plt.imread('images/fish-bike.jpg')\n",
      "plt.imshow(im)\n",
      "currentAxis = plt.gca()\n",
      "\n",
      "det = df.iloc[i]\n",
      "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
      "print coords\n",
      "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
      "\n",
      "det = df.iloc[j]\n",
      "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
      "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Top detection:\n",
        "name\n",
        "person             0.426542\n",
        "swimming trunks   -1.269269\n",
        "rubber eraser     -1.270367\n",
        "face powder       -1.358900\n",
        "horizontal bar    -1.372487\n",
        "dtype: float32\n",
        "\n",
        "Second-best detection:\n",
        "name\n",
        "person             0.426542\n",
        "swimming trunks   -1.269269\n",
        "rubber eraser     -1.270367\n",
        "face powder       -1.358900\n",
        "horizontal bar    -1.372487\n",
        "dtype: float32\n",
        "((230, 9), 100, 141)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<matplotlib.patches.Rectangle at 0x7f4966444190>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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thDWaac9cIcI2AmSiEJOUsLBmXOjhDBc8+jmFKSJ45dJVYkm/E7/Kgyal3bGp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       "text": [
        "<matplotlib.figure.Figure at 0x7f49674c11d0>"
       ]
      }
     ],
     "prompt_number": 34
    }
   ],
   "metadata": {}
  }
 ]
}